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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Dammak S, Gulstene S, Palma DA, Mattonen SA, Senan S, Ward AD. Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics. Sci Rep 2024; 14:3758. [PMID: 38355768 PMCID: PMC10866960 DOI: 10.1038/s41598-024-52828-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion's longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.
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Affiliation(s)
- Salma Dammak
- Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada.
- School of Biomedical Engineering, Western University, London, ON, Canada.
| | - Stephanie Gulstene
- Department of Radiation Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - David A Palma
- Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada
- Department of Radiation Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Sarah A Mattonen
- Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Suresh Senan
- Department of Radiation Oncology, VU Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Aaron D Ward
- Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Huang BT, Wang Y, Lin PX. Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy. J Cancer Res Clin Oncol 2024; 150:34. [PMID: 38277078 PMCID: PMC10817845 DOI: 10.1007/s00432-023-05536-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters. METHODS Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction. RESULTS With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735-0.914), 0.886 (95% CI 0.790-0.948), and 0.914 (95% CI 0.825-0.966) in the training cohort, and 0.757 (95% CI 0.580-0.887), 0.818 (95% CI 0.648-0.929), and 0.843 (95% CI 0.677-0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility. CONCLUSION A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.
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Affiliation(s)
- Bao-Tian Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China.
| | - Ying Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Pei-Xian Lin
- Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
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Jiang X, Su N, Quan S, E L, Li R. Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease. Acad Radiol 2023; 30:2598-2605. [PMID: 36868880 DOI: 10.1016/j.acra.2023.01.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/29/2023] [Accepted: 01/29/2023] [Indexed: 03/05/2023]
Abstract
PURPOSE To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest. MATERIALS AND METHODS Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results. GAP I, II, and III have 137, 36, and 11 cases, respectively. The cases in GAP Ⅱ and Ⅲ were then combined into one group, and the two groups of patients were randomly divided into the training and testing groups with a 7:3 ratio. The radiomics features were extracted using AK software. Multivariate logistic regression analysis was then conducted to establish a radiomics model. A nomogram model was established on the basis of Rad-score and clinical factors (age and gender). RESULTS For the radiomics model, four significant radiomics features were selected to construct the model and showed excellent ability to differentiate GAP I from GAP Ⅱ and Ⅲ in both the training group (the area under the curve [AUC] = 0.803, 95% confidence interval [CI]: 0.724-0.874) and testing group (AUC = 0.801, 95% CI:0.663-0.912). The nomogram model that combined clinical factors and radiomics features improved higher accuracy of both training (88.4% vs. 82.1%) and testing (83.3% vs. 79.2%). CONCLUSION The disease severity assessment of patients with CTD-ILD can be evaluated by applying the radiomics method based on CT images. The nomogram model demonstrates better performance for predicting the GAP staging.
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Affiliation(s)
- Xiaopeng Jiang
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China; Tongji Hospital, Tongji Medical College, Huazhong University, China
| | - Ningling Su
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China; Tongji Hospital, Tongji Medical College, Huazhong University, China
| | - Shuai Quan
- GE HealthCare China (Shanghai), Shanghai, 210000, China
| | - Linning E
- Affiliated Longhua People's Hospital, Southern Medical University (Longhua People's Hospital), Shenzhen, 518110, China
| | - Rui Li
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China; Tongji Hospital, Tongji Medical College, Huazhong University, China.
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Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
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Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
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Kunkyab T, Mou B, Jirasek A, Haston C, Andrews J, Thomas S, Hyde D. Radiomic analysis for early differentiation of lung cancer recurrence from fibrosis in patients treated with lung stereotactic ablative radiotherapy. Phys Med Biol 2023; 68:165015. [PMID: 37164024 DOI: 10.1088/1361-6560/acd431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 05/10/2023] [Indexed: 05/12/2023]
Abstract
Objective. The development of radiation-induced fibrosis after stereotactic ablative radiotherapy (SABR) can obscure follow-up images and delay detection of a local recurrence in early-stage lung cancer patients. The objective of this study was to develop a radiomics model for computer-assisted detection of local recurrence and fibrosis for an earlier timepoint (<1 year) after the SABR treatment.Approach. This retrospective clinical study included CT images (n= 107) of 66 patients treated with SABR. A z-score normalization technique was used for radiomic feature standardization across scanner protocols. The training set for the radiomics model consisted of CT images (66 patients; 22 recurrences and 44 fibrosis) obtained at 24 months (median) follow-up. The test set included CT-images of 41 patients acquired at 5-12 months follow-up. Combinations of four widely used machine learning techniques (support vector machines, gradient boosting, random forests (RF), and logistic regression) and feature selection methods (Relief feature scoring, maximum relevance minimum redundancy, mutual information maximization, forward feature selection, and LASSO) were investigated. Pyradiomics was used to extract 106 radiomic features from the CT-images for feature selection and classification.Main results. An RF + LASSO model scored the highest in terms of AUC (0.87) and obtained a sensitivity of 75% and a specificity of 88% in identifying a local recurrence in the test set. In the training set, 86% accuracy was achieved using five-fold cross-validation. Delong's test indicated that AUC achieved by the RF+LASSO is significantly better than 11 other machine learning models presented here. The top three radiomic features: interquartile range (first order), Cluster Prominence (GLCM), and Autocorrelation (GLCM), were revealed as differentiating a recurrence from fibrosis with this model.Significance. The radiomics model selected, out of multiple machine learning and feature selection algorithms, was able to differentiate a recurrence from fibrosis in earlier follow-up CT-images with a high specificity rate and satisfactory sensitivity performance.
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Affiliation(s)
- Tenzin Kunkyab
- Department of Physics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | | | - Andrew Jirasek
- Department of Physics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Christina Haston
- Department of Physics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Jeff Andrews
- Department of Statistics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | | | - Derek Hyde
- Department of Physics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
- BC Cancer-Kelowna, Canada
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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Zhang Q, Wang K, Zhou Z, Qin G, Wang L, Li P, Sher D, Jiang S, Wang J. Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model. Front Oncol 2022; 12:955712. [PMID: 36248979 PMCID: PMC9557184 DOI: 10.3389/fonc.2022.955712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/05/2022] [Indexed: 12/03/2022] Open
Abstract
Objectives Accurate identifying head and neck squamous cell cancer (HNSCC) patients at high risk of local persistence/recurrence (P/R) is of importance for personalized patient management. Here we developed a multi-objective, multi-classifier radiomics model for early HNSCC local P/R prediction based on post-treatment PET/CT scans and clinical data. Materials and methods We retrospectively identified 328 individuals (69 patients have local P/R) with HNSCC treated with definitive radiation therapy at our institution. The median follow-up from treatment completion to the first surveillance PET/CT imaging was 114 days (range: 82-159 days). Post-treatment PET/CT scans were reviewed and contoured for all patients. For each imaging modality, we extracted 257 radiomic features to build a multi-objective radiomics model with sensitivity, specificity, and feature sparsity as objectives for model training. Multiple representative classifiers were combined to construct the predictive model. The output probabilities of models built with features from various modalities were fused together to make the final prediction. Results We built and evaluated three single-modality models and two multi-modality models. The combination of PET, CT, and clinical data in the multi-objective, multi-classifier radiomics model trended towards the best prediction performance, with a sensitivity of 93%, specificity of 83%, accuracy of 85%, and AUC of 0.94. Conclusion Our study demonstrates the feasibility of employing a multi-objective, multi-classifier radiomics model with PET/CT radiomic features and clinical data to predict outcomes for patients with HNSCC after radiation therapy. The proposed prediction model shows the potential to detect cancer local P/R early after radiation therapy.
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Affiliation(s)
- Qiongwen Zhang
- Department of Head and Neck Oncology, Department of Radiation Oncology, Cancer Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Zhiguo Zhou
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Genggeng Qin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Lei Wang
- Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Ping Li
- Department of Head and Neck Oncology, Department of Radiation Oncology, Cancer Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - David Sher
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
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Mancosu P, Lambri N, Castiglioni I, Dei D, Iori M, Loiacono D, Russo S, Talamonti C, Villaggi E, Scorsetti M, Avanzo M. Applications of artificial intelligence in stereotactic body radiation therapy. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7e18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Abstract
This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation. The selected papers were subdivided into four sections, representative of the whole radiotherapy process: ‘AI in SBRT target and organs at risk contouring’, ‘AI in SBRT planning’, ‘AI during the SBRT delivery’, and ‘AI for outcome prediction after SBRT’. Each section summarises the challenges, as well as limits and needs for improvement to achieve better integration of AI tools in the clinical workflow.
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Kodama T, Arimura H, Shirakawa Y, Ninomiya K, Yoshitake T, Shioyama Y. Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy. Thorac Cancer 2022; 13:2117-2126. [PMID: 35711108 PMCID: PMC9346172 DOI: 10.1111/1759-7714.14483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/04/2022] [Indexed: 12/25/2022] Open
Abstract
Background This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non‐small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR). Methods We divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet‐based features (WF), topology‐based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high‐risk and low‐risk groups using the medians of the radiomics scores in the training dataset. Results For the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC. Conclusion The iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.
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Affiliation(s)
- Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yuko Shirakawa
- National Hospital Organization Kyushu Cancer CenterFukuokaJapan
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Tadamasa Yoshitake
- Department of Clinical Radiology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
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Hsu W, Sohn JH. Using Radiomics for Risk Stratification: Where We Need to Go. Radiology 2022; 302:435-437. [PMID: 34726541 PMCID: PMC8805519 DOI: 10.1148/radiol.2021212085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 08/29/2021] [Accepted: 09/01/2021] [Indexed: 02/03/2023]
Affiliation(s)
- William Hsu
- From the Department of Radiology, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (W.H.); and Department of Radiology, University of California, San Francisco, San Francisco, Calif (J.H.S.)
| | - Jae Ho Sohn
- From the Department of Radiology, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (W.H.); and Department of Radiology, University of California, San Francisco, San Francisco, Calif (J.H.S.)
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15
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Luo LM, Huang BT, Chen CZ, Wang Y, Su CH, Peng GB, Zeng CB, Wu YX, Wang RH, Huang K, Qiu ZH. A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters. Front Oncol 2022; 11:819047. [PMID: 35174072 PMCID: PMC8841423 DOI: 10.3389/fonc.2021.819047] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 12/31/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters. METHODS The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve. RESULTS The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility. CONCLUSIONS The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.
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Affiliation(s)
- Li-Mei Luo
- Department of Radiation Oncology, Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Bao-Tian Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Chuang-Zhen Chen
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ying Wang
- Department of Radiation Oncology, Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Chuang-Huang Su
- Department of Radiation Oncology, Shantou Central Hospital, Shantou, China
| | - Guo-Bo Peng
- Department of Radiation Oncology, Meizhou People’s Hospital (Huangtang Hospital), Meizhou Academy of Medical Sciences, Meizhou, China
| | - Cheng-Bing Zeng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yan-Xuan Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ruo-Heng Wang
- Department of Radiation Oncology, Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Kang Huang
- Department of Radiation Oncology, Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Zi-Han Qiu
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
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16
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Lee K, Le T, Hau E, Hanna GG, Gee H, Vinod S, Dammak S, Palma D, Ong A, Yeghiaian-Alvandi R, Buck J, Lim R. A systematic review into the radiological features predicting local recurrence after stereotactic ablative body radiotherapy (SABR) in patients with non-small cell lung cancer (NSCLC): Local recurrence features of NSCLC post-SABR. Int J Radiat Oncol Biol Phys 2021; 113:40-59. [PMID: 34879247 DOI: 10.1016/j.ijrobp.2021.11.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND PURPOSE Post-treatment surveillance for local recurrence (LR) following SABR can include both fluorodeoxyglucose-positron emission tomography (FDG-PET) and computed tomography (CT). Radiation-induced lung injury (RILI) shares a similar appearance to LR after treatment making the detection of LR on imaging difficult for clinicians. We aimed to summarise radiological features of CT and FDG-PET predicting LR, and to evaluate radiomics as another tool for detecting LR. METHODS AND MATERIALS We searched MEDLINE, EMBASE and PubMed databases for published studies and Web of Science, Wiley Online and Science Direct databases for conference abstracts that had patient populations with NSCLC and reported post-SABR radiological features of FDG-PET or CT and radiomics from either FDG-PET or CT. Studies for inclusion were independently reviewed by two authors. RESULTS Across 32 relevant studies, the incidence of LR was 13% (222/1726). On CT, certain gross radiological appearances, and kinetic features of changes in size, diameter, volume or 3 consecutive rises in volume of mass-like consolidation are suggestive of LR. Particular regard should be made for the presence of any ≥3 high-risk features (HRF) on CT or the individual HRF of enlarging opacity at ≥12 month's post-SABR as being highly suspicious of LR. On FDG-PET a relative reduction of <5% of SUVmax from baseline in the first 12 months or cut-offs of SUVmax >5 and SUVmean >3.44 after 12 months can indicate LR. There is limited evidence available to corroborate radiomic features suggestive of LR. CONCLUSION This research has identified common features of LR compared to RILI which may aid in early and accurate detection of LR post-SABR; further research is required to validate these findings.
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Affiliation(s)
- Katherine Lee
- Westmead Hospital, Sydney, New South Wales, Australia; Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
| | - Tue Le
- Radiation Oncology - Mid North Coast Cancer Institute, Port Macquarie, New South Wales, Australia
| | - Eric Hau
- Sydney West Radiation Oncology Network, Sydney, New South Wales, Australia; Westmead Clinical School, The University of Sydney, Sydney, New South Wales, Australia; Westmead Institute of Medical Research, Sydney, New South Wales, Australia
| | - Gerard G Hanna
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Victoria, Australia
| | - Harriet Gee
- Sydney West Radiation Oncology Network, Sydney, New South Wales, Australia; Children's Medical Research Institute, Sydney, New South Wales, Australia; The University of Sydney, Sydney, New South Wales, Australia
| | - Shalini Vinod
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia; South Western Sydney Clinical School, University of New South Wales, Liverpool, New South Wales, Australia
| | - Salma Dammak
- The School of Biomedical Engineering, Western University, London, Ontario, Canada; Baines Imaging Research Laboratory, London Regional Cancer Program, London, Ontario, Canada
| | - David Palma
- Division of Radiation Oncology, Western University, London, Ontario, Canada
| | - Anselm Ong
- Department of Radiation Oncology, The Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead Sydney, New South Wales, Australia
| | | | - Jacqueline Buck
- Department of Medical Oncology, Nepean Cancer Care Centre, Nepean Hospital, Kingswood, New South Wales, Australia
| | - Rebecca Lim
- Department of Radiology, Westmead Hospital, Sydney, New South Wales, Australia
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Jaberipour M, Soliman H, Sahgal A, Sadeghi-Naini A. A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning. Sci Rep 2021; 11:21620. [PMID: 34732781 PMCID: PMC8566533 DOI: 10.1038/s41598-021-01024-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 10/21/2021] [Indexed: 12/14/2022] Open
Abstract
This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.
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Affiliation(s)
- Majid Jaberipour
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada
| | - Hany Soliman
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Arjun Sahgal
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Ali Sadeghi-Naini
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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19
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Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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Affiliation(s)
- A A Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D A Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A A Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F N Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
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20
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Tunali I, Tan Y, Gray JE, Katsoulakis E, Eschrich SA, Saller J, Aerts HJWL, Boyle T, Qi J, Guvenis A, Gillies RJ, Schabath MB. Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer. JNCI Cancer Spectr 2021; 5:pkab048. [PMID: 34409252 PMCID: PMC8363765 DOI: 10.1093/jncics/pkab048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/30/2021] [Accepted: 04/16/2021] [Indexed: 12/26/2022] Open
Abstract
Background Immunotherapy yields survival benefit for some advanced stage non-small cell lung cancer (NSCLC) patients. Because highly predictive biomarkers of immunotherapy response are an unmet clinical need, we used pretreatment radiomics and clinical data to train and validate a parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Methods Three cohorts of NSCLC patients treated with immunotherapy were analyzed: training (n = 180), validation 1 (n = 90), and validation 2 (n = 62). The most informative clinical and radiomic features were subjected to decision tree analysis, which stratified patients into risk groups of low, moderate, high, and very high risk of death after initiation of immunotherapy. All statistical tests were 2-sided. Results The very high-risk group was associated with extremely poor overall survival (OS) in validation cohorts 1 (hazard ratio [HR] = 5.35, 95% confidence interval [CI] = 2.14 to 13.36; 1-year OS = 11.1%, 95% CI = 1.9% to 29.8%; 3-year OS = 0%) and 2 (HR = 13.81, 95% CI = 2.58 to 73.93; 1-year OS = 47.6%, 95% CI = 18.2% to 72.4%; 3-year OS = 0%) when compared with the low-risk group (HR = 1.00) in validation cohorts 1 (1-year OS = 85.0%, 95% CI = 60.4% to 94.9%; 3-year OS = 38.9%, 95% CI = 17.1% to 60.3%) and 2 (1-year OS = 80.2%, 95% CI = 40.3% to 94.8%; 3-year OS = 40.1%, 95% CI = 1.3% to 83.5%). The most informative radiomic feature, gray-level co-occurrence matrix (GLCM) inverse difference, was positively associated with hypoxia-related carbonic anhydrase 9 using gene-expression profiling and immunohistochemistry. Conclusion Utilizing standard-of-care imaging and clinical data, we identified and validated a novel parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Based on this model, clinicians can identify patients who are unlikely to respond to immunotherapy.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Jhanelle E Gray
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | - Steven A Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - James Saller
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Theresa Boyle
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jin Qi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew B Schabath
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Tunali I, Gillies RJ, Schabath MB. Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a039537. [PMID: 33431509 PMCID: PMC8288444 DOI: 10.1101/cshperspect.a039537] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer patients. Most medical images are stored digitally in a standardized Digital Imaging and Communications in Medicine format that can be readily accessed and used for qualitative and quantitative analysis. Over the several last decades, medical images have been shown to contain complementary and interchangeable data orthogonal to other sources such as pathology, hematology, genomics, and/or proteomics. As such, "radiomics" has emerged as a field of research that involves the process of converting standard-of-care images into quantitative image-based data that can be merged with other data sources and subsequently analyzed using conventional biostatistics or artificial intelligence (AI) methods. As radiomic features capture biological and pathophysiological information, these quantitative radiomic features have shown to provide rapid and accurate noninvasive biomarkers for lung cancer risk prediction, diagnostics, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics and emerging AI methods in lung cancer research are highlighted and discussed including advantages, challenges, and pitfalls.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
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22
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Interobserver Variability in the Computed Tomography Assessment of Pulmonary Injury and Tumor Recurrence After Stereotactic Body Radiotherapy. J Thorac Imaging 2021; 35:302-308. [PMID: 32168165 DOI: 10.1097/rti.0000000000000495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE To evaluate the interobserver agreement of chest computed tomography (CT) findings in the diagnosis of expected changes and local recurrence after stereotactic body radiation therapy (SBRT) in patients with early-stage lung cancer or a single pulmonary metastasis. MATERIALS AND METHODS A total of 54 patients with early-stage lung cancer or pulmonary metastasis who were treated with SBRT from 2007 to 2015 were included. The exclusion criteria were patients who presented with pulmonary infection during follow-up and patients who underwent a single CT during follow-up. The imaging features on CT were assessed by 3 blinded radiologists at the following 2 time points after SBRT: (a) early follow-up and (b) late follow-up (≥6 mo). The radiologists classified the findings as expected changes after SBRT or recurrence. Interobserver agreement was assessed by kappa and Wilcoxon statistics. RESULTS A total of 13 women and 41 men with a mean age of 75.3 (±8.9) years were selected. The total and per fraction SBRT doses were 54 Gy (interquartile range: 45 to 54) and 18 Gy (interquartile range: 15 to 18), respectively. All expected changes and findings suggestive of recurrence had an almost perfect agreement (κ>0.85) among readers, except for diffuse consolidation in the early period (κ=0.65). CONCLUSION CT findings demonstrate high interobserver agreement for expected changes and for findings indicating recurrence after SBRT.
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Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians. J Pers Med 2021; 11:jpm11070602. [PMID: 34202096 PMCID: PMC8306026 DOI: 10.3390/jpm11070602] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
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Lu L, Sun SH, Yang H, E L, Guo P, Schwartz LH, Zhao B. Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data. ACTA ACUST UNITED AC 2021; 6:223-230. [PMID: 32548300 PMCID: PMC7289249 DOI: 10.18383/j.tom.2020.00017] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We investigated the performance of multiple radiomics feature extractors/software on predicting epidermal growth factor receptor mutation status in 228 patients with non–small cell lung cancer from publicly available data sets in The Cancer Imaging Archive. The imaging and clinical data were split into training (n = 105) and validation cohorts (n = 123). Two of the most cited open-source feature extractors, IBEX (1563 features) and Pyradiomics (1319 features), and our in-house software, Columbia Image Feature Extractor (CIFE) (1160 features), were used to extract radiomics features. Univariate and multivariate analyses were performed sequentially to predict EGFR mutation status using each individual feature extractor. Our univariate analysis integrated an unsupervised clustering method to identify nonredundant and informative candidate features for the creation of prediction models by multivariate analyses. In training, unsupervised clustering-based univariate analysis identified 5, 6, and 4 features from IBEX, Pyradiomics, and CIFE as candidate features, respectively. Multivariate prediction models using these features from IBEX, Pyradiomics, and CIFE yielded similar areas under the receiver operating characteristic curve of 0.68, 0.67, and 0.69. However, in validation, areas under the receiver operating characteristic curve of multivariate prediction models from IBEX, Pyradiomics, and CIFE decreased to 0.54, 0.56 and 0.64, respectively. Different feature extractors select different radiomics features, which leads to prediction models with varying performance. However, correlation between those selected features from different extractors may indicate these features measure similar imaging phenotypes associated with similar biological characteristics. Overall, attention should be paid to the generalizability of individual radiomics features and radiomics prediction models.
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Affiliation(s)
- Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Shawn H Sun
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Hao Yang
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Linning E
- Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Pingzhen Guo
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
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Michalet M, Azria D, Tardieu M, Tibermacine H, Nougaret S. Radiomics in radiation oncology for gynecological malignancies: a review of literature. Br J Radiol 2021; 94:20210032. [PMID: 33882246 DOI: 10.1259/bjr.20210032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.
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Affiliation(s)
- Morgan Michalet
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
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El Ayachy R, Giraud N, Giraud P, Durdux C, Giraud P, Burgun A, Bibault JE. The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up. Front Oncol 2021; 11:603595. [PMID: 34026602 PMCID: PMC8131863 DOI: 10.3389/fonc.2021.603595] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care. Methods A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020 Results We identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications. Conclusion Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
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Affiliation(s)
- Radouane El Ayachy
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Nicolas Giraud
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France.,Radiation Oncology Department, Haut-Lévêque Hospital, CHU de Bordeaux, Pessac, France
| | - Paul Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Catherine Durdux
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Anita Burgun
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Jean Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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Zhang Y, Fan Q, Guo Y, Zhu K. Eight-gene signature predicts recurrence in lung adenocarcinoma. Cancer Biomark 2021; 28:447-457. [PMID: 32508318 DOI: 10.3233/cbm-190329] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD. OBJECTIVE We performed this study to construct a model to predict risk of recurrence in LUAD. METHODS RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213). Differentially expressed genes (DEGs) between recurrent and non-recurrent tumors in the training set were identified. Recurrence-associated DEGs were selected using multivariate Cox regression analysis. The recurrence risk model that identifies patients at low and high risk for recurrence was constructed, followed by the validation of its performance in the validation set and a microarray dataset. RESULTS In total, 378 DEGs, including 20 recurrence-associated DEGs, were identified between the recurrent and non-recurrent tumors in the training set. The signatures of 8 genes (including AZGP1, INPP5J, MYBPH, SPIB, GUCA2A, HTR1B, SLC15A1 and TNFSF11) were used to construct the prognostic model to assess the risk of recurrence. This model indicated that patients with high risk scores had shorter recurrence-free survival time compared with patients with low risk scores. ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset. This prognostic model showed high predictive power and performance in predicting recurrence in LUAD. CONCLUSION We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.
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Affiliation(s)
- Yongjian Zhang
- Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.,Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Qiang Fan
- Department of Oncology Radiology, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.,Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yingying Guo
- Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Koujun Zhu
- Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
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Vlaskou Badra E, Baumgartl M, Fabiano S, Jongen A, Guckenberger M. Stereotactic radiotherapy for early stage non-small cell lung cancer: current standards and ongoing research. Transl Lung Cancer Res 2021; 10:1930-1949. [PMID: 34012804 PMCID: PMC8107760 DOI: 10.21037/tlcr-20-860] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Stereotactic body radiation therapy (SBRT) allows for the non-invasive and precise delivery of ablative radiation dose. The use and availability of SBRT has increased rapidly over the past decades. SBRT has been proven to be a safe, effective and efficient treatment for early stage non-small cell lung cancer (NSCLC) and is presently considered the standard of care in the treatment of medically or functionally inoperable patients. Evidence from prospective randomized trials on the optimal treatment of patients deemed medically operable remains owing, as three trials comparing SBRT to surgery in this cohort were terminated prematurely due to poor accrual. Yet, SBRT in early stage NSCLC is associated with favorable toxicity profiles and excellent rates of local control, prompting discussion in regard of the treatment of medically operable patients, where the standard of care currently remains surgical resection. Although local control in early stage NSCLC after SBRT is high, distant failure remains an issue, prompting research interest to the combination of SBRT and systemic treatment. Evolving advances in SBRT technology further facilitate the safe treatment of patients with medically or anatomically challenging situations. In this review article, we discuss international guidelines and the current standard of care, ongoing clinical challenges and future directions from the clinical and technical point of view.
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Affiliation(s)
- Eugenia Vlaskou Badra
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Michael Baumgartl
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Silvia Fabiano
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Aurélien Jongen
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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30
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Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 109:1647-1656. [PMID: 33333202 PMCID: PMC7965338 DOI: 10.1016/j.ijrobp.2020.12.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/20/2020] [Accepted: 12/11/2020] [Indexed: 01/07/2023]
Abstract
PURPOSE To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy. METHODS AND MATERIALS Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance. RESULTS Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ2 = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ2 = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ2 = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures. CONCLUSION The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.
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31
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Gao L, Li Y, Zhai Z, Liang T, Zhang Q, Xie S, Chen H. Radiomics study on pulmonary infarction mimicking community-acquired pneumonia. CLINICAL RESPIRATORY JOURNAL 2021; 15:661-669. [PMID: 33686798 DOI: 10.1111/crj.13341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/13/2020] [Accepted: 02/15/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION AND OBJECTIVES Pulmonary infarction (PI) shares similar symptoms and imaging presentations with community-acquired pneumonia (CAP), which might delay diagnosis and lead to devastating consequences. Noncontrast computed tomography (CT) is the first-line examination for the patients with the respiratory symptoms. This study aimed to investigate a radiomics method to differentiate PI from CAP using noncontrast-enhanced CT. METHODS Noncontrast-enhanced CT images of 54 patients with PI and 64 patients with CAP were retrospectively selected. All patients were confirmed using computed tomography pulmonary angiography (CTPA). A radiomics model was built with 18 texture features that showed significant differences between PI and CAP patients. For comparison, a clinical model using clinical biomarkers and an integrated model combining the radiomics and clinical biomarkers were also generated. An experienced radiologist performed diagnoses using the noncontrast-enhanced CT images. The parameters of the models were generated using a training dataset of 61 patients, whereas the performance of the models was evaluated using receiver operating characteristic (ROC) analysis and Harrell's concordance index (C-index) applied to a separate validation dataset of 57 patients. RESULTS The integrated model achieved the best performance (C-index 0.760, sensitivity 0.703, specificity 0.867, positive predictive value [PPV] 0.826, and negative predictive value [NPV] 0.765). The radiomics model was better than both the clinical model and the radiologist's interpretations (C-index 0.721, 0.707, 0.665, respectively; sensitivity 0.667, 0.630, 0.593; specificity 0.800, 0.785, 0.733; PPV 0.750, 0.739, 0.667; and NPV 0.727, 0.706, 0.667). CONCLUSIONS Radiomics features generated from noncontrast-enhanced CT images allow PI to be differentiated from CAP with considerable accuracy. The radiomics-based method could provide useful information in clinical practice.
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Affiliation(s)
- Li Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yuze Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zhenguo Zhai
- Department of Respiratory and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Tian Liang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Qiang Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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Fusco R, Granata V, Mazzei MA, Meglio ND, Roscio DD, Moroni C, Monti R, Cappabianca C, Picone C, Neri E, Coppola F, Montanino A, Grassi R, Petrillo A, Miele V. Quantitative imaging decision support (QIDS TM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control 2021; 28:1073274820985786. [PMID: 33567876 PMCID: PMC8482708 DOI: 10.1177/1073274820985786] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Objective: To evaluate the consistency of the quantitative imaging decision support (QIDSTM) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan. Materials and Methods: We included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDSTM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman’s correlation coefficient were performed. QIDSTM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change. Results: Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared. Conclusion: In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDSTM were significant and robust predictors of RECIST response.
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Affiliation(s)
- Roberta Fusco
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vincenza Granata
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Maria Antonietta Mazzei
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Nunzia Di Meglio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Davide Del Roscio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Chiara Moroni
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
| | - Riccardo Monti
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carlotta Cappabianca
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carmine Picone
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Emanuele Neri
- Division of Radiodiagnostic, 9257"Azienda Ospedaliera Universitaria Pisana," Pisa, Italy
| | - Francesca Coppola
- Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine (DIMES), "S. Orsola Hospital, University of Bologna," Bologna, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli," Naples, Italy
| | - Roberta Grassi
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vittorio Miele
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
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Vaugier L, Mirabel X, Martel-Lafay I, Racadot S, Carrie C, Vendrely V, Mahé MA, Senellart H, Raoul JL, Campion L, Rio E. Radiosensitizing Chemotherapy (Irinotecan) with Stereotactic Body Radiation Therapy for the Treatment of Inoperable Liver and/or Lung Metastases of Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13020248. [PMID: 33440832 PMCID: PMC7827408 DOI: 10.3390/cancers13020248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Stereotactic body radiotherapy (SBRT) is a recognized treatment for liver or lung metastases, but radiosensitivity of colorectal cancer could be lower than other primary cancers. We postulated that local responses could be improved by SBRT with a concomitant radiosensitizing agent (irinotecan). RADIOSTEREO-CAMPTO was a prospective multi-center phase 2 trial conducted between 2008 and 2013. We confirmed that SBRT-Irinotecan was a short, effective and well-tolerated treatment, with no worsening of the quality of life. It allowed for several months of chemotherapy-free periods despite most patients receiving multiple prior lines of treatment. Radiosensitizing irinotecan was able to compensate for lower SBRT dose than nowadays used for liver and lung metastases and could be an interesting regimen in case of tumour-surrounding healthy tissues requiring limited radiation dose. Abstract Background: Stereotactic body radiotherapy (SBRT) is a recognized treatment for colorectal cancer (CRC) metastases. We postulated that local responses could be improved by SBRT with a concomitant radiosensitizing agent (irinotecan). Methods: RADIOSTEREO-CAMPTO was a prospective multi-center phase 2 trial investigating SBRT (40–48 Gy in 4 fractions) for liver and/or lung inoperable CRC oligometastases (≤3), combined with two weekly intravenous infusions of 40 mg/m2 Irinotecan. Primary outcome was the objective local response rate as per RECIST. Secondary outcomes were early and late toxicities, EORTC QLQ-C30 quality of life, local control and overall survival. Results: Forty-four patients with 51 lesions (liver = 39, lungs = 12) were included. Median age was 69 years (46–84); 37 patients (84%) had received at least two prior chemotherapy treatments. Median follow-up was 48.9 months. One patient with two lung lesions was lost during follow-up. Assuming maximum bias hypothesis, the objective local response rate in ITT was 86.3% (44/51—95% CI: [76.8–95.7]) or 82.4% (42/51—95% CI: [71.9–92.8]). The observed local response rate was 85.7% (42/49—95% CI: [75.9–95.5]). The 1 and 2-year local (distant) progression-free survivals were 84.2% (38.4%) and 67.4% (21.3%), respectively. The 1 and 2-year overall survivals were 97.5% and 75.5%. There were no severe acute or late reactions. The EORTC questionnaire scores did not significantly worsen during or after treatment. Conclusions: SBRT with irinotecan was well tolerated with promising results despite heavily pretreated patients.
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Affiliation(s)
- Loïg Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France; (L.V.); (M.-A.M.)
| | - Xavier Mirabel
- Department of Radiation Oncology, Centre Oscar Lambret, 59000 Lille, France;
| | - Isabelle Martel-Lafay
- Department of Radiation Oncology, Institut Léon Bérard, 69008 Lyon, France; (I.M.-L.); (S.R.); (C.C.)
| | - Séverine Racadot
- Department of Radiation Oncology, Institut Léon Bérard, 69008 Lyon, France; (I.M.-L.); (S.R.); (C.C.)
| | - Christian Carrie
- Department of Radiation Oncology, Institut Léon Bérard, 69008 Lyon, France; (I.M.-L.); (S.R.); (C.C.)
| | - Véronique Vendrely
- Department of Radiation Oncology, Centre Hospitalo-Universitaire Hôpital Saint André, 33000 Bordeaux, France;
| | - Marc-André Mahé
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France; (L.V.); (M.-A.M.)
| | - Hélène Senellart
- Department of Medical Oncology, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France; (H.S.); (J.-L.R.)
| | - Jean-Luc Raoul
- Department of Medical Oncology, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France; (H.S.); (J.-L.R.)
| | - Loïc Campion
- Department of Biostatistics, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France;
- Centre de Recherche en Cancérologie Nantes-Angers (CRCNA), UMR 1232 Inserm—6299 CNRS, Institut de Recherche en Santé de l’Université de Nantes, 44000 Nantes, France
| | - Emmanuel Rio
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 St-Herblain, France; (L.V.); (M.-A.M.)
- Correspondence: ; Tel.: +33-240-679-900
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Interobserver agreement between interpretations of acute changes after lung stereotactic body radiotherapy. Strahlenther Onkol 2020; 197:423-428. [PMID: 33231713 DOI: 10.1007/s00066-020-01711-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/02/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE Stereotactic body radiation therapy (SBRT) is an effective treatment modality for inoperable early-stage lung cancer or metastatic lung lesions. Post-SBRT, acute radiological lung changes sometimes mimic tumor progression, so over-investigation may be applied. We aimed to reveal the interobserver agreement among physicians regarding acute radiographic changes on CT of the thorax obtained shortly after SBRT MATERIALS AND METHODS: Radiologic images of 20 lesions treated with SBRT were evaluated for acute lung changes. Two physicians, one senior and one junior, from diagnostic radiology, radiation oncology, nuclear medicine, and chest disease departments reviewed these images. The final interpretations were categorized as stable, regression/consolidation, progressive disease, and SBRT-related changes. The evaluations of the physicians were compared with the experienced reference radiation oncologist. The gold standard was accepted as the reference physician's final score. Unweighted Cohen's kappa (κ) coefficient was used for assessing interobserver agreement between physicians. RESULTS The evaluations of the physicians were compared with the reference radiation oncologist. The strongest coherence coefficient was found with the senior radiation oncologist (κ: 0.72). The kappa coefficients between the junior radiation oncologist, junior nuclear medicine physician, and the reference physician were 0.61 and 0.55, respectively. The disciplines with the lowest kappa coefficients were junior chest disease and senior radiologist, and the kappa values were 0.37 and 0.44, respectively. CONCLUSION Disciplines dealing with lung cancer treatment may not be aware of the various radiologic changes after SBRT or inexperienced in interpreting them from recurrence. Therefore, physicians must have detailed radiotherapy information such as planning target volume (PTV), dose/fractionation, etc. In addition, final evaluations should be performed in the multidisciplinary team dealing with the treatment of the patient.
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Vaugier L, Ferrer L, Mengue L, Jouglar E. Radiomics for radiation oncologists: are we ready to go? BJR Open 2020; 2:20190046. [PMID: 33178967 PMCID: PMC7594896 DOI: 10.1259/bjro.20190046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/06/2020] [Accepted: 03/09/2020] [Indexed: 12/19/2022] Open
Abstract
Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.
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Affiliation(s)
- Loïg Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Ludovic Ferrer
- Department of Medical Physics, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Laurence Mengue
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Emmanuel Jouglar
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
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Shen JX, Zhou Q, Chen ZH, Chen QF, Chen SL, Feng ST, Li X, Wu TF, Peng S, Kuang M. Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation. Transl Oncol 2020; 14:100866. [PMID: 33074127 PMCID: PMC7569222 DOI: 10.1016/j.tranon.2020.100866] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES To develop a radiomics algorithm, improving the performance of detecting recurrence, based on posttreatment CT images within one month and at suspicious time during follow-up. MATERIALS AND METHODS A total of 114 patients with 228 images were randomly split (7:3) into training and validation cohort. Radiomics algorithm was trained using machine learning, based on difference-in-difference (DD) features extracted from tumor and liver regions of interest on posttreatment CTs within one month after resection or ablation and when suspected recurrent lesion was observed but cannot be confirmed as HCC during follow-up. The performance was evaluated by area under the receiver operating characteristic curve (AUC) and was compared among radiomics algorithm, change of alpha-fetoprotein (AFP) and combined model of both. Five-folded cross validation (CV) was used to present the training error. RESULTS A radiomics algorithm was established by 34 DD features selected by random forest and multivariable logistic models and showed a better AUC than that of change of AFP (0.89 [95% CI: 0.78, 1.00] vs 0.63 [95% CI: 0.42, 0.84], P = .04) and similar with the combined model in detecting recurrence in the validation set. Five-folded CV error in the validation cohort was 21% for the algorithm and 26% for the changes of AFP. CONCLUSIONS The algorithm integrated radiomic features of posttreatment CT showed superior performance to that of conventional AFP and may act as a potential marker in the early detecting recurrence of HCC.
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Affiliation(s)
- Jing-Xian Shen
- State Key Laboratory of Oncology in Southern China, Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhi-Hang Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qiao-Feng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shu-Ling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Ming Kuang
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept. Eur Radiol 2020; 31:1987-1998. [PMID: 33025174 PMCID: PMC7979612 DOI: 10.1007/s00330-020-07293-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/30/2020] [Accepted: 09/14/2020] [Indexed: 01/04/2023]
Abstract
Objective To retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT). Methods Sixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated. Results Values for some radiomics features were significantly lower (p < 0.05) and those of other radiomics features were significantly higher (p = 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity). Conclusion The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis. Key Points • Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of almost 100%. • Extent of fibrosis on HRCT and a combined model of different visual HRCT-ILD features perform worse in predicting GAP stage. • The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features on HRCT, which are not recognized by visual analysis. Electronic supplementary material The online version of this article (10.1007/s00330-020-07293-8) contains supplementary material, which is available to authorized users.
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Schick U, Lucia F, Bourbonne V, Dissaux G, Pradier O, Jaouen V, Tixier F, Visvikis D, Hatt M. Use of radiomics in the radiation oncology setting: Where do we stand and what do we need? Cancer Radiother 2020; 24:755-761. [DOI: 10.1016/j.canrad.2020.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/14/2022]
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Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy. Biomedicines 2020; 8:biomedicines8090371. [PMID: 32972006 PMCID: PMC7556039 DOI: 10.3390/biomedicines8090371] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/31/2022] Open
Abstract
Therapeutic response is evaluated using the diameter of tumors and quantitative parameters of 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET). Tumor response to molecular-targeted drugs and immune checkpoint inhibitors is different from conventional chemotherapy in terms of temporal metabolic alteration and morphological change after the therapy. Cancer stem cells, immunologically competent cells, and metabolism of cancer are considered targets of novel therapy. Accumulation of FDG reflects the glucose metabolism of cancer cells as well as immune cells in the tumor microenvironment, which differs among patients according to the individual immune function; however, FDG-PET could evaluate the viability of the tumor as a whole. On the other hand, specific imaging and cell tracking of cancer cell or immunological cell subsets does not elucidate tumor response in a complexed interaction in the tumor microenvironment. Considering tumor heterogeneity and individual variation in therapeutic response, a radiomics approach with quantitative features of multimodal images and deep learning algorithm with reference to pathologic and genetic data has the potential to improve response assessment for emerging cancer therapy.
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Qiu Q, Duan J, Deng H, Han Z, Gu J, Yue NJ, Yin Y. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery. Front Oncol 2020; 10:1398. [PMID: 32850451 PMCID: PMC7431604 DOI: 10.3389/fonc.2020.01398] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/02/2020] [Indexed: 12/24/2022] Open
Abstract
Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongbin Deng
- Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhujun Han
- Department of Radiation Oncology, Yantai Yuhuangding Hospital, Yantai, China
| | - Jiabing Gu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ning J Yue
- Department of Radiation Oncology, The Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Christie JR, Lang P, Zelko LM, Palma DA, Abdelrazek M, Mattonen SA. Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making. Can Assoc Radiol J 2020; 72:86-97. [PMID: 32735493 DOI: 10.1177/0846537120941434] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.
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Affiliation(s)
- Jaryd R Christie
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada
| | - Pencilla Lang
- Division of Radiation Oncology, 6221Western University, London, Ontario, Canada
| | - Lauren M Zelko
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada
| | - David A Palma
- Division of Radiation Oncology, 6221Western University, London, Ontario, Canada
| | - Mohamed Abdelrazek
- Department of Medical Imaging, 6221Western University, London, Ontario, Canada
| | - Sarah A Mattonen
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada.,Department of Oncology, 6221Western University, London, Ontario, Canada
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Yang WC, Hsu FM, Yang PC. Precision radiotherapy for non-small cell lung cancer. J Biomed Sci 2020; 27:82. [PMID: 32693792 PMCID: PMC7374898 DOI: 10.1186/s12929-020-00676-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/17/2020] [Indexed: 02/07/2023] Open
Abstract
Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information. Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics. This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.
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Affiliation(s)
- Wen-Chi Yang
- Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, Taiwan.,Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Feng-Ming Hsu
- Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, Taiwan. .,Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Pan-Chyr Yang
- Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan. .,Department of Internal Medicine, National Taiwan University Hospital, No.1 Sec 1, Jen-Ai Rd, Taipei, 100, Taiwan.
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Plodkowski AJ, Araujo-Filho JAB, Simmers CDA, Girshman J, Raj M, Zheng J, Rimner A, Ginsberg MS. Pre-treatment CT imaging in stage IIIA lung cancer: Can we predict local recurrence after definitive chemoradiotherapy? Clin Imaging 2020; 69:133-138. [PMID: 32721848 DOI: 10.1016/j.clinimag.2020.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/19/2020] [Accepted: 07/13/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVES The aim of this study was to delineate computed tomography (CT) features of stage IIIA non-small cell lung cancers on pre-treatment staging studies and identify features that could predict local recurrence after definitive concurrent chemoradiotherapy. MATERIALS AND METHODS We retrospectively reviewed pre- and post-treatment CT scans for 91 patients with Stage IIIA non-small cell lung cancer undergoing definitive concurrent chemoradiotherapy. Pre-treatment CT qualitative features were evaluated by consensus. The primary endpoint was local recurrence as determined on post-treatment CT scans along with the radiotherapy fields. Local recurrence was defined as intrathoracic in-field and marginal as opposed to out-of-field failures. Competing risk regressions were used to examine associations between CT features and recurrence. RESULTS The median follow-up was 51.5 months (range 2.4-111.2). Median overall survival was 25.6 months (95% CI: 20.4-30). At last follow-up, 72 (79.1%) patients had died, 48 (52.7%) had in-field recurrence, and 30 (32.9%) presented with out-of-field recurrence. On pre-treatment CT scans, tumors presenting as pulmonary consolidations (hazard ratio = 2.34, 95% CI: 1.05-5.22; p 0.038) were more likely to have in-field failure. Tumors with 50-100% necrosis (hazard ratio = 0.15, 95% CI: 0.02-1.06) were associated with decreased out-of-field failure (overall p = 0.038). However, these were rare features in our sample which limit the ability of these features to be associated with such outcomes. CONCLUSIONS Pre-treatment CT features alone are limited in predicting locoregional recurrence. Larger studies using quantitative tools are needed to predict such outcomes.
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Affiliation(s)
- Andrew J Plodkowski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | | | - Cameron D A Simmers
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Jeffrey Girshman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Micheal Raj
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Junting Zheng
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Michelle S Ginsberg
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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Cheng Z, Nakatsugawa M, Zhou XC, Hu C, Greco S, Kiess A, Page B, Alcorn S, Haller J, Utsunomiya K, Sugiyama S, Fu W, Wong J, Lee J, McNutt T, Quon H. Utility of a Clinical Decision Support System in Weight Loss Prediction After Head and Neck Cancer Radiotherapy. JCO Clin Cancer Inform 2020; 3:1-11. [PMID: 30860866 DOI: 10.1200/cci.18.00058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE To evaluate the utility of a clinical decision support system (CDSS) using a weight loss prediction model. METHODS A prediction model for significant weight loss (loss of greater than or equal to 7.5% of body mass at 3-month post radiotherapy) was created with clinical, dosimetric, and radiomics predictors from 63 patients in an independent training data set (accuracy, 0.78; area under the curve [AUC], 0.81) using least absolute shrinkage and selection operator logistic regression. Four physicians with varying experience levels were then recruited to evaluate 100 patients in an independent validation data set of head and neck cancer twice (ie, a pre-post design): first without and then with the aid of a CDSS derived from the prediction model. At both evaluations, physicians were asked to predict the development (yes/no) and probability of significant weight loss for each patient on the basis of patient characteristics, including pretreatment dysphagia and weight loss and information from the treatment plan. At the second evaluation, physicians were also provided with the prediction model's results for weight loss probability. Physicians' predictions were compared with actual weight loss, and accuracy and AUC were investigated between the two evaluations. RESULTS The mean accuracy of the physicians' ability to identify patients who will experience significant weight loss (yes/no) increased from 0.58 (range, 0.47 to 0.63) to 0.63 (range, 0.58 to 0.72) with the CDSS ( P = .06). The AUC of weight loss probability predicted by physicians significantly increased from 0.56 (range, 0.46 to 0.64) to 0.69 (range, 0.63 to 0.73) with the aid of the CDSS ( P < .05). Specifically, more improvement was observed among less-experienced physicians ( P < .01). CONCLUSION Our preliminary results demonstrate that physicians' decisions may be improved by a weight loss CDSS model, especially among less-experienced physicians. Additional study with a larger cohort of patients and more participating physicians is thus warranted for understanding the usefulness of CDSSs.
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Affiliation(s)
- Zhi Cheng
- Johns Hopkins University, Baltimore, MD
| | | | | | - Chen Hu
- Johns Hopkins University, Baltimore, MD
| | | | - Ana Kiess
- Johns Hopkins University, Baltimore, MD
| | | | | | - John Haller
- Canon Medical Research USA, Vernon Hills, IL
| | | | | | - Wei Fu
- Johns Hopkins University, Baltimore, MD
| | - John Wong
- Johns Hopkins University, Baltimore, MD
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Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
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Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
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Cui S, Tseng HH, Pakela J, Ten Haken RK, Naqa IE. Introduction to machine and deep learning for medical physicists. Med Phys 2020; 47:e127-e147. [PMID: 32418339 PMCID: PMC7331753 DOI: 10.1002/mp.14140] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/23/2020] [Accepted: 03/03/2020] [Indexed: 01/01/2023] Open
Abstract
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state-of-the-art tools should be able to solve pressing problems in modern radiation oncology. Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed. Machine learning can be categorized based on the underlying task into supervised learning, unsupervised learning, or reinforcement learning; each of these categories has its own input/output dataset characteristics and aims to solve different classes of problems in medical physics ranging from automation of processes to predictive analytics. It is recognized that data size requirements may vary depending on the specific medical physics application and the nature of the algorithms applied. Data processing, which is a crucial step for model stability and precision, should be performed before training the model. Deep learning as a subset of ML is able to learn multilevel representations from raw input data, eliminating the necessity for hand crafted features in classical ML. It can be thought of as an extension of the classical linear models but with multilayer (deep) structures and nonlinear activation functions. The logic of going "deeper" is related to learning complex data structures and its realization has been aided by recent advancements in parallel computing architectures and the development of more robust optimization methods for efficient training of these algorithms. Model validation is an essential part of ML/DL model building. Without it, the model being developed cannot be easily trusted to generalize to unseen data. Whenever applying ML/DL, one should keep in mind, according to Amara's law, that humans may tend to overestimate the ability of a technology in the short term and underestimate its capability in the long term. To establish ML/DL role into standard clinical workflow, models considering balance between accuracy and interpretability should be developed. Machine learning/DL algorithms have potential in numerous radiation oncology applications, including automatizing mundane procedures, improving efficiency and safety of auto-contouring, treatment planning, quality assurance, motion management, and outcome predictions. Medical physicists have been at the frontiers of technology translation into medicine and they ought to be prepared to embrace the inevitable role of ML/DL in the practice of radiation oncology and lead its clinical implementation.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Julia Pakela
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
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48
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Frerker B, Hildebrandt G. <p>Distinguishing Radiation Pneumonitis from Local Tumour Recurrence Following SBRT for Lung Cancer</p>. REPORTS IN MEDICAL IMAGING 2020. [DOI: 10.2147/rmi.s176901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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49
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Khawaja A, Bartholmai BJ, Rajagopalan S, Karwoski RA, Varghese C, Maldonado F, Peikert T. Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification. J Thorac Dis 2020; 12:3303-3316. [PMID: 32642254 PMCID: PMC7330769 DOI: 10.21037/jtd.2020.03.105] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Despite multiple recent advances, the diagnosis and management of lung cancer remain challenging and it continues to be the deadliest malignancy. In 2011, the National Lung Screening Trial (NLST) reported 20% reduction in lung cancer related mortality using annual low dose chest computed tomography (CT). These results led to the approval and nationwide establishment of lung cancer CT-based lung cancer screening programs. These findings have been further validated by the recently published Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) and Multicentric Italian Lung Detection (MILD) trials, the latter showing benefit of screening even beyond the 5 years. However, the implementation of lung cancer screening has been impeded by several challenges, including the differentiation between benign and malignant nodules, the large number of false positive studies and the detection of indolent, potentially clinically insignificant lung cancers (overdiagnosis). Hence, the development of non-invasive strategies to accurately classify and risk stratify screen-detected pulmonary nodules in order to individualize clinical management remains a high priority area of research. Radiomics is a recently coined term which refers to the process of imaging feature extraction and quantitative analysis of clinical diagnostic images to characterize the nodule phenotype beyond what is possible with conventional radiologist assessment. Even though it is still in early phase, several studies have already demonstrated that radiomics approaches are potentially useful for lung nodule classification, risk stratification, individualized management and prediction of overall prognosis. The goal of this review is to summarize the current literature regarding the radiomics of screen-detected lung nodules, highlight potential challenges and discuss its clinical application along with future goals and challenges.
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Affiliation(s)
- Ali Khawaja
- Divison of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Cyril Varghese
- Divison of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Fabien Maldonado
- Division of Pulmonary and Critical Care Medicine, Vanderbilt University, Nashville, TN, USA
| | - Tobias Peikert
- Divison of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
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50
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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